With the development of the digital technology, there has been an explosion in use of digital multimedia data. Analogue audio and video equipment is gradually in the process of being replaced by its digital successors. With help of the digital storage and Internet connections, the distribution of multimedia data and applications is becoming much easier and faster, so copyright issues are increasingly important to the owners of the digital content data, and for this reason techniques are being developed for modifying digital data (“watermarking” the data), in such a way that the fact that the data has been modified can be detected. Digital watermarking technology makes it easier for copyright to be enforced, because it makes it easier for the copyright owner to prove that the data originates from him. However, digital watermarking is not only used for copyright protection, but also for indexing, captioning, data hiding, etc.
A watermarking system usually has two stages: (i) watermark embedding and (ii) watermark detection/extraction. When it is required to protect the ownership of the image, the embedded watermark is detected and extracted from the modified image.
A first key issue in technology for embedding watermarks is the choice of the domain (“workspace”) in which watermark embedding should be performed.
For example, existing watermarking techniques operate in the spatial domain, discrete cosine transform (DCT) domain, Mellin-Fourier transform domain, wavelet domain, etc. A further key issue is the selection of the pixels, blocks or transform coefficients where the watermarks should be hidden.
Desirably, a watermark is embedded in a host image by modifying the image so that the modifications in the image are not visible. Such “imperceptibility” is one of the most important requirements in image watermarking systems. Also, desirably, the watermark should be hard to remove (to “attack”).
We now review some known watermarking techniques in relation to these two desiderata.
(1) Spatial domain watermarking is the most straightforward watermarking technique. An advantage of the spatial domain techniques is that they can be easily applied to any image, regardless of subsequent processing. One approach is called the Least-Significant-Bits modification method (LSB). In this technique the watermark may be embedded anywhere in the host image, so there is a high channel capacity and a small watermark object may be embedded multiple times. Even if most of these objects are lost due to attacks, a single surviving watermark would be considered a success. LSB substitution, however, despite its simplicity brings a host of drawbacks. Although it may survive transformations such as cropping, any addition of noise or lossy compression will totally remove the watermark. An even better attack is simply to set the LSB bits of each pixel, since this would fully remove the watermark with negligible impact on the original data. Furthermore, once the embedding algorithm is known, the embedded watermark can be easily discovered. An improvement on basic LSB substitution is to use a pseudo-random number generator to determine the pixels to be used for embedding based on a given “seed” or key. More advanced spatial domain methods exist too, such as correlation based techniques. Another disadvantage of spatial techniques is that the embedding technique for producing them cannot be published without making the watermark more easily removable. In addition, adaptive watermarking techniques are difficult in the spatial domain, as it is hard to distinguish between smooth and noisy regions.
(2) Discrete Cosine Transform (DCT) domain watermarking has been widely studied in the context of JPEG and MPEG, normally in the mid-frequency AC components. Embedding operations in the DCT domain are often robust to JPEG and MPEG compression, so the watermark can resist JPEG/MPEG attacks more easily. Watermarking in DCT domain offers the possibility of embedding watermarks directly in the compressed format, so as to minimise the computation time. However, previous studies on visibility in DCT domain compression predict the visible impact of the watermark on the watermarked image. Usually, the watermark is embedded in the low-frequency AC coefficients in the DCT domain.
(3) Wavelet domain techniques exploit the Wavelet analysis signal processing method which has been popularly applied in image processing in recent decades. Wavelet analysis is usually based on multi-resolution analysis (MRA) which analyzes an image in detail in the frequency domain. The wavelet transform consists of a multiscale spatial-frequency decomposition of an image, e.g. into four bands such as an approximate image LL and three detail images LH, HL and HH. The MRA is compatible with perception by human eyes, therefore, it is helpful in managing a good selection of watermark embedding locations in the original image in terms of robustness versus visibility. Wavelets are also key in the ongoing compression standard JPEG2000, so wavelet domain watermarking also has the advantage of robustness to JPEG2000 compression.
(4) Most watermarking techniques encounter serious problems in extracting watermarks after an affined geometric distortion, i.e. image rotation, scaling and translation (RST), which is called mis-synchronization. For this reason Mellin-Fourier Transform domain watermarking techniques have been introduced since this transform is invariant under RST transformations and even their combination and permutation in any order. Watermarking in this way may also have the advantage of combining the watermarking embedding process with methods dealing with solving geometric distortion problems. However, one drawback is that this watermarking technique increases the complexity of watermarking very much so practical adoption of such techniques may not be possible. Another disadvantage is that it may not be easy to achieve imperceptibility of the watermarks.
The final judgement on the “imperceptibility” of watermarks relies on human eyes. Therefore, the workspace and the techniques used within that workspace should ideally be selected based on our Human Vision System (HVS). However, none of the above-mentioned watermarking domains are directly based on HVS, but instead are derived from frequency-based mathematical functions. Although some known wavelet watermarking techniques select the watermark embedding locations in accordance with HVS by selecting certain frequency bands for watermark embedding, these techniques do not fully satisfy the requirements of HVS and do not guarantee the invisibility of the embedding.